Vilma Lukoseviciene used to measure success by bugs caught. The closer to release, the higher the stakes, the better the catch. That was the job. Then she started using AI and the job description quietly shifted.
I used to think being a strong Senior Test Engineer meant finding as many bugs as possible before release,” she says. “Now I think it means preventing those bugs by using AI to think earlier and smarter about risk.
Vilma is a Senior QA and Test Engineer at Gritmind. The shift she is describing is from reactive to proactive: AI lets her apply testing instincts at the start of a feature, not just at the end. It is a fundamentally different relationship with the work, one where the value is not speed but judgment applied earlier in the process.
That distinction is worth sitting with.
Research consistently finds that women adopt AI tools at lower rates and with more skepticism than men, more likely to ask whether a tool is trustworthy, more cautious about replacing human judgment with automated output. A meta-analysis published by SSIR found the gap spans 18 studies and holds across industries. The conventional read is that this caution is a liability, a barrier to adoption that organizations need to close.
A growing body of research disagrees. Women’s skepticism about AI isn’t a knowledge gap. It’s a quality control instinct. And at Gritmind, we’ve watched it produce better products.
We put the same questions to the women at Gritmind across engineering, product, HR, design, and finance. We asked them about the moment their relationship with AI shifted, what they’ve built with it, and what they’d tell someone just starting out. Here’s what came back.
AI gives you access. Knowing what to build is still on you.
Frances Thomas is a Senior Product Manager at Gritmind. At last year’s internal hackathon, she built and deployed working software in a single day alongside other PMs and designers, with no software engineers on the team. She used Cursor to stand up a full front end and backend that used an LLM to summarize user interviews.
Instead of seeing AI as a replacement for you, think of it as an enhancement of you. I was pretty hesitant to adopt AI. But once I shifted that mindset, everything changed. I report out on technical issues to non-technical stakeholders. Prompting AI to simplify messaging and remove ambiguous wording has been genuinely useful. The work is still mine. It just gets there faster.
Most conversations about AI and productivity focus on speed: how fast the turnaround, how many tools in the stack. Frances is pointing at something the speed metrics don’t capture. The judgment call about what to build, what to cut, what the user actually needs. That part has not changed. It is just more exposed now that everything else is faster.
Sarune Cernikeviciene is a Frontend Developer at Gritmind. Her starting point was honest skepticism.
I used to think using AI tools in development felt like cheating, taking a shortcut. Now I think learning how to use AI effectively is a skill worth learning. It speeds up my work, helps me explore different approaches, and gives me new ways to solve problems. To use it effectively, start with a clear vision and well-defined requirements. You need to know exactly what needs to be done. Avoid getting stuck in endless loops with AI while coding. Over-relying on it can compromise code quality and clarity
That last point is worth noting. Sarune is not saying AI does the thinking. She is saying it helps her do more of it, faster, across more of the stack. The discipline is still hers.
Faster synthesis, better decisions: what AI looks like across design and product
Mikayla Zeppenfeld is a Designer at Gritmind. She used to think getting started on designs was the hardest part of kicking off a project. That framing has changed.
I used to think that getting started on designs was the hardest part about kicking off a project. Now I think it’s incredible to have the ability to upload my messy short-hand notes from a client call to an AI tool and have it generate wireframes that can serve as a platform for iterative designs. I’ve converted to using AI tools in every step of the design process. During discovery, I’ll use tools like Granola to synthesize user interview notes and surface pain points and opportunities. Adding AI to my daily workflow has allowed me to deliver work faster and think outside the box.
She is not using AI to generate final designs. She is using it to compress the distance between a messy client call and a design surface she can actually work from. The design decisions are still hers. What changed is how quickly she gets to them.
Jenna Stworzyjanek is a Product Manager at Gritmind. Every discovery call she runs gets transcribed and fed into a structured workflow. Specialized agents build out the backlog from the raw conversation. She reviews, edits, and brings it to the team. What used to take a day now takes thirty minutes.
My most used workflow right now is to transcribe my discovery calls and requirements gathering meetings using AI, add the transcripts into a document repository, and have my specialized agents build out my backlog. Then, I review the backlog, make edits, and bring them to the team. I’m working on a root cause analysis application for investigating incidents in autonomous vehicles. The application has multiple agents that each specialize in a different area of the vehicle.The ability to make myself a ‘proof-reader’ rather than a writer has freed up time to work alongside our engineering team on the build and iterate faster.
One of our clients wrote about this shift directly As AI closes the gap between intention and execution, cross-functional teams don’t disappear, they become teams of builders. People who can move end-to-end on problems that used to require handoffs. The specialized competencies are still there. The job description has changed.
AI judgment, not AI speed, is the real competitive advantage
None of these five adopted AI because it was exciting. They adopted it because they could see specifically where it was useful, and specifically where it wasn’t. Vilma isn’t using it to replace her QA instincts; she’s using it to apply them earlier. Frances isn’t using it to make product decisions; she’s using it so she can make better ones faster. Mikayla isn’t using it to design; she’s using it to get to the design.
That’s not caution. That’s discernment. And it’s the same quality that produces better AI products in production, where “good enough in a demo” runs into real users, real data, and real failure modes that nobody thought to test for.
Kristina Stepanaviciene, one of Gritmind’s developers, put it cleanly. That’s not a description of a tool. It’s a description of a practitioner who knows the difference and uses that knowledge to decide where to spend her attention.
AI replaced parts of my job that don’t deserve my time. That freed me to focus on parts that genuinely do.
The research calls this skepticism. We’d call it a standard. And it’s exactly the standard that keeps AI products honest.
AI in production is not a speed problem. It’s a judgment problem. The teams that use AI to ship products that actually work, that earn user trust and survive real data, are the ones that asked the uncomfortable questions early. At Gritmind, that’s not an instinct we’re trying to train out of anyone. It’s the whole point.
If you’re building an AI product and want a senior team that knows when to slow down, and why that makes delivery faster in the end, Gritmind can help you validate your approach before it hits production.